DMSAT-1 (Dubai Municipality Satellite) is a high-performance small microsatellite designed to perform multi‐spectral observations in visual and near-infrared bands for aerosol and greenhouse gas monitoring. DMSAT-1 has two independent telescopes (one with 0-degree polarization and the second with 90-degree polarization) each containing a linear polarizer. The polarizers are mounted perpendicularly. DMSAT-1 captures images in three bands – Blue, Red, and Near-Infrared. This paper puts forward customized algorithms for Mohammed Bin Rashid Space Centre (MBRSC), which are developed in Python for radiometric validation and tested on images captured by the primary instrument (polarimeters) on the DMSAT-1 microsatellite. Radiometric validation includes the validation of the Signal-Noise Ratio, Non-Uniformity Correction, and Modulation Transfer Function of the images. The proposed method validates the images across different bands as well as different polarizers and confirms that satellite images can be readily used.
Over the last decade, Dubai emirate witnessed a vast, rapidly growing population, that doubled since 2008. Nowadays, Dubai considers as the most populated emirate within the United Arab Emirates (UAE). With such an increasing population and new urban developments, sustainable urban planning procedures play an essential role in Dubai's environmental quality such as air quality, and pollution. Therefore, this study will utilize the Remote Sensing and Geographic Information system (GIS) to investigate Dubai's environmental quality by addressing and locating green areas and pollution percentages within each district. The study methodology is divided into three steps. First, Landsat Satellite medium spatial resolution and multi-spectral imagery will be used as an input for segmentation and object-based analysis. Considering the spectral and spatial signatures for green areas machine learning techniques will be adopted to select the most significant features to classify and extract green areas. Second, using environmental relational indices, green areas percentages will be quantitatively compared to Sentinel air quality data, such as NO2 and SO2, as well as the population density maps. Finally, GIS techniques will be used to create Dubai Environmental Critical Map (DECM), to locate districts with limited green areas and high pollution to improve environmental standards. The study results can be used as a measure for the municipality policymakers to ensure sustainable urban development for a healthy living.
Urbanization is a spatiotemporal process that has significant role in economic, social, and environmental structures. Spatiotemporal analysis for urban growth is vital for city management planning. With highly recognized financial and social developing trends, Dubai City, UAE appears as one of most challenging cities in terms of research and preparation toward a smart city aspect. Integrated technologies of remote sensing and geographic information system (GIS) facilitate urban growth detection and its relation to population distribution. In this study Multi-temporal, medium-resolution Landsat images were used to detect and analyze the urbanization trend in Dubai over the last three decades(1986-2019). Moreover, the influence of urbanization on the aspects of smart city tendency was investigated. The study methodology consisted of three parts. First, classification algorithms along with change detection, segmentation, and extraction of urban areas were used to obtain land Use/land Cover (LULC) maps. Second, Shannon's entropy was used to investigate Dubai's growth toward compact or sprawl city based on two city centers and a major highway. Finally, CA-Markov, associated with the digital elevation model and road map of Dubai, was used to simulate the urban change for 2030, 2050, and 2100. With more than 90% overall accuracy, the statistical analysis for LULC percentages and Shannons entropy values indicated that Dubai experienced a considerable increase in urban fabric while maintaining a compact growth. CA-Markov model estimated 3% urban growth by 2030, which would result in potential loss of green areas and open spaces. This study could be used in improving planning and management methods to achieve sustainable urban growth.
The United Arab Emirates (UAE) is one of the fastest agriculture economical growing country in the world. One aspect of this agriculture growth is the development of the date palm trees sector in the UAE. The date palm tree is considered one of the oldest and most widely cultivated tree, which is commercially the most important tree in the life of its people and their heritage. Moreover, the date palm tree was believed to be a part of the UAE strategy to control desertification. With this huge investment and interest in palm trees in the UAE, there is limited knowledge of the actual tree counts and their exact spatial locations, which is a requirement for any agricultural census. WorldView-3 satellite images were used to develop an algorithm to detect and count palm trees in the UAE. The processing was done in two steps: the first step is to detect palm trees which involved supervised classification using maximum likelihood with four feature classes: Red, Blue, Green and Near infrared (NIR) bands associated with palm trees objects taken by the labeling. The second step is to count palm trees which involved extracting local spatial maxima of Laplacian blob from Normalized Difference Vegetation Index (NDVI) masking. The algorithm was tested in different regions of interest in AlAin city, part of the capital Emirate Abu Dhabi. The algorithm and final results are compared with ground truth images for accuracy assessment. The results were satisfactory with an accuracy of 89% and higher and very minimum negligible misclassification.
Over the past years, remote sensing imagery made the earth monitoring more effective and valuable through developing different algorithms for feature extraction. One of the significant features are water surfaces. Water features extraction such as pools, lakes and gulfs gained a considerable attention over the past years, as water plays critical role for surviving, planning and protecting water resources. Past worth efforts in water extraction from remote sensed images mainly faced the challenge of misclassification, especially with shadows. Shadows are typical noise objects for water, extraction, as they have almost identical spectrum characteristics, which result difficulty to discriminate between water and shadows in a remote sensing image, especially in the urban region such as Dubai.
Therefore, water extraction algorithm is developed in order to extract water surfaces accurately with shadows elimination. The detection is based on spectral information such as water indices (WIs), and morphological operations. Water indices are used to discriminate water surfaces from lands based on combining two or more water indices such as Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Saturation-value Difference Index (NSVDI), used at an optimum threshold. The morphological operators will be performed using opening by reconstruction to discriminate between water and shadows at an optimum threshold. Both Water Indices and morphological operation results will be infused together in one image that result a binary image of water objects.
The algorithm and final results are compared with ground truth image for accuracy assessment, the results were satisfactory with an accuracy of 95% and higher and very minimum negligible shadows appeared. Moreover the resultant image transformed into vector features in order to create a shape file that can be used and viewed in google earth and Geo software.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.